Our mission
There's a growing need in science for better written software.
America’s biopharmaceutical R&D engine consumes over $100 billion every year, and the costs continue to rise. The past decade has brought a cambrian explosion of new technology to accelerate the development of new medicines and scientific discovery, producing enormous amounts of data.
Take cryogenic electron microscopy for example. This went from collecting single integrated images to several terabytes of data per day. By 2025, the total amount of genomics data alone is is expected to equal or exceed the total of three other major producers of data: astronomy, YouTube, and Twitter.
To keep up, the use of machine learning, deep learning, and AI has exploded and become an essential part of the biotech industry. Bench scientists turn to bioinformaticians. Half of the most recent science papers are software-intensive projects, written by scientists who were never formally trained.
Our scientists are struggling.
We're delaying the future by 5 to 10 years.
We can't fail our best and brightest.
In 2022, the FDA issued 161 letters for failing to backup data and follow protocols. The NIH just updated standards on data integrity.
We've seen lack of proper foundations result in lost data, analyses taking much longer than necessary, and researchers being limited in how effectively they can work with software and data.
The solution?
We need to train our scientists with the most mission critical skills so they can get back to doing what they do best, science.
Computing workflows need to follow the same practices as lab projects and notebooks, with organized data, documented steps, and the project structured for reproducibility.
50%
of scientists fail to reproduce one of their own experiements.
Nature (2016)
70%
of scientists fail to reproduce the results of their peer's studies.
Nature (2016)
We've seen this first-hand. Vijay has been teaching, advising, and investing in biotech companies for years and has witnessed poor code result in lost data, analyses taking much longer than necessary, and researchers being limited in how effectively they can work with software and data.
The solution?
Computing workflows need to follow the same practices as lab projects and notebooks, with organized data, documented steps, and the project structured for reproducibility.
